A cloud-based MODFLOW service for aquifer management decision support

Jones, David; Jones, Norm; Greer, J.K.; Nelson, Jim

doi:10.1016/j.cageo.2015.02.014

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…In order to ensure the stability and speediness of the system, DSS streamlines the forecast time and precision; hence, it also needs improving. With further development of computer technology and enhancement of the computing ability of hardware, real-time and accurate disaster forecast models will be created in the near future [24], and DSS with the strengths of this model will be constantly improved. This will be one of the major research directions of our team in the future.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Construction of a Decision Support System Based on GP Services, Using a Warning–Judgment Module as an Example

Sang

Liu

Leng

et al. 2017

IJGI

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Decision-making departments need more detailed and timely data in order to meet the needs of emergency response. Sichuan province is an area that frequently suffers natural disasters, and many disasters are caused by rainfall. This study establishes a decision support system (DSS) based on geoprocessing (GP) services, which can locate the region that overran the rainfall threshold and provide the population or property analysis, query, map plot, and path analysis functions. Most of the functions of the system are developed on the basis of geoprocessing services. This paper uses the warning-judgment module as an example to introduce the structure and function of the DSS system. The system satisfies the demands of real-time data acquisition, calculation, analysis, and presentation.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Construction of a Decision Support System Based on GP Services, Using a Warning–Judgment Module as an Example

Sang

Liu

Leng

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…Cloud-based MODFLOW ArcGIS [213,214] None Several methods This is based on the simulation modules only. This limitation does not permit extensive interpolation due to the lack of a numerical model.…”

Section: Technique Numerical Model Spatial Interpolation Limitationmentioning

confidence: 99%

A Review of Groundwater Management Models with a Focus on IoT-Based Systems

et al. 2021

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Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system which is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

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“…Cloud-based MODFLOW ArcGIS [197][198] None Several methods This is based on the simulation modules only. This limitation does not permit extensive interpolation due to the lack of a numerical model…”

Section: Technique Numerical Model Spatial Interpolation Limitationmentioning

confidence: 99%

Groundwater Resources Management Modelling: A Review

Aderemi¹,

Olwal²,

Ndambuki³

et al. 2021

Preprint

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Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often changes as a result of land usage, abstraction as well as variation in climate. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity, and minimising the effects of drought has become a major challenge for researchers as well as water managers. Furthermore, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater resource model have been revealed in the management of groundwater resources. To solve these challenges, many conventional solutions such as numerical techniques have been proffered for groundwater modelling. Also, the use of data-driven techniques such as machine learning is gaining more attraction to solve these aforementioned challenges. Thus, this has made efficient data gathering essential to maintain da-ta-driven groundwater resources management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data At the moment, some pieces of the literature revealed the groundwater managers lack an efficient, scalable and real-time groundwater management system to gather the required data. Also, the literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management methods, nonetheless, this paper seeks to highlight the current groundwater management models as well as the IoT contributions

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A cloud-based MODFLOW service for aquifer management decision support

Cited by 15 publications

References 14 publications

Construction of a Decision Support System Based on GP Services, Using a Warning–Judgment Module as an Example

Construction of a Decision Support System Based on GP Services, Using a Warning–Judgment Module as an Example

A Review of Groundwater Management Models with a Focus on IoT-Based Systems

Groundwater Resources Management Modelling: A Review

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